jochembrouwer

> Checking facts is hard and takes time.

This is a reasoning skill and I fear we are losing this skill if we do not update our education systems (all over the world) very fast. Children will use AI, and we should also see this as a tool. However, children now need to learn how to verify the output of the tool, and this is something what they do not do (and they also are not incentivized to do so). Which needs to change, otherwise the next generation of people exiting high school can use AI, but they have not learned how to reason themselves.

This also happens I guess in companies, AI is the simple way, the reasoning is the "hard" part which costs energy, and people need to be incentivized to do so. I have no idea how to create that incentive though.

So the take-away here is that we (as humans) try to model these AIs like humans, but eventually these AIs get better. Which to me seems like a logical conclusion if they can do "things" (like "learning" or pattern matching) much faster than we can (the compute). Then language in LLMs is a bottleneck, the AI is constrained by the language, and thus if we want to scale further we could let AI create its own language (we would then have to translate whatever it creates back to a language we understand). It is the same for instance if we check the language of Inuit (people who live in the north and make temorary shelters like igloos in the snow) they have multiple words/verbs to describe the snow, while in English we only have one (?): snow. In English we don't need more words (we can explain snow state using multiple words) but for the Inuit language it makes sense to create these new terms (would also make it easier and faster to communicate). So in some sense, all languages are then "newspeak" to whatever a general language is what researches or AI might come up with. If this sounds dumb let me know, but if you know some research in this general language direction (I'd assume general AI research) would love to see it!